Infectious disease preparedness and control are critically dependent on knowledge of disease transmission patterns across space and time. The study of these patterns is greatly dependent on high-resolution public health data and adaptable tools for statistical inference. This Project aims to improve knowledge on spatiotemporal transmission patterns for infectious diseases in the United States and abroad leading to improved control strategies and better preparedness. This Project will contribute the first components of an integrated decision support pathway created by the MIDAS Center. It will create new insights in disease transmission patterns, develop innovative statistical and parameter estimation tools and will create a comprehensive high-resolution data resource for the MIDAS Network, policy makers, and the community at large. Specific aims are: 1) acquisition, curation, and integration of new large scale epidemiologic, vital statistics, and demographic data to improve the case-law of infectious disease dynamics, resulting in an essential data resource for modeling; 2) creation of innovative tools for the analysis of spatiotemporal disease transmission patterns, using spatiotemporal statistics and flexible non-linear parameter estimation methods; and 3) inferring individual level transmission dynamics from newly integrated disease data, contact patterns, and genetic data using advanced computational methods. This will result in new knowledge and scientific insight in the impact of pathogen natural history, vaccination, and other factors on spatiotemporal dynamics of distinct childhood infections and also in individual level transmission networks. This Project will shift the paradigm for the study of infectious disease transmission dynamics from ad hoc analyses of specific situations constrained by limited data availability to the systematic exploration of transmission dynamics for a wide range of pathogens and geographies. This Project will build on previous successes of the MIDAS Center to integrate and make available large scale public health datasets and to create innovative non-linear parameter estimation methods. This Project will advance the field by accelerating resources and tools for infectious disease modeling, resulting in better preparedness and control of existing and emerging threats